RussellSpitzer edited a comment on issue #4217:
URL: https://github.com/apache/iceberg/issues/4217#issuecomment-1050935112


   Could you paste what q7-v2.4 is? My guess would be if the join types are
   swapped then the size estimation is occurring differently in the Hive and
   Iceberg code *or* the pushdown is not working correctly. Spark should be
   planning the same logical join in both cases, the only difference should be
   the transition to a physical plan which would be dependent on the source
   responding with how much information is on each side of the join.
   
   If the predicate pushdown isn't working that would happen, or it could be
   that our size estimate code has a bug with certain predicates. A full Spark
   EXPLAIN of the query should show the predicate portion at least, the Spark
   UI should have the input sizes reported.
   
   One more note: Hive has a much more general notion of filter compatibility 
and it could be that
   Iceberg is failing to pushdown predicates because it is more strict. For 
example if the
   predicate uses a Int but the column is a String, Hive may be implicitly 
converting and applying
   the predicate when Iceberg does not ... Would need to see the plan for the 
hive and Iceberg tables to know for sure
   
   On Fri, Feb 25, 2022 at 2:31 AM andrey-mindrin ***@***.***>
   wrote:
   
   > Additional information. Total size of data is about 300 Gb in parquet,
   > tried different options for iceberg tables including vectorization,
   > distributed by hash and range. Iceberg tables created with a query like
   >
   > create table ice_table using iceberg partition by (column) location
   > ‘hdfs://location’
   > 
tblproperties(‘vectorization-enabled’=true,’write.parquet.compression-codec’=’snappy’,’
   > write.distribution-mode’=’hash’) as select * from hive_table
   >
   > Analyzing query plans in Spark for query q7-v2.4 shows different join
   > types – SortMergeJoin in Iceberg and BroadcastHashJoin in Hive tables.
   >
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